A mobile phone, cell phone, cellphone, or hand phone, sometimes shortened to simply mobile, cell or just phone, is a portable telephone that can make and receive calls over a radio frequency link while the user is moving within a telephone service area.
The first handheld mobile phone was demonstrated by John F. Mitchell and Martin Cooper of Motorola in 1973, using a handset weighing c. 2 kilograms (4.4 lbs).
In 1979, Nippon Telegraph and Telephone (NTT) launched the world's first cellular network in Japan. In 1983, the DynaTAC 8000x was the first commercially available handheld mobile phone. From 1983 to 2014, worldwide mobile phone subscriptions grew to over seven billion—enough to provide one for every person on Earth.
In first quarter of 2016, the top smartphone developers worldwide were Samsung, Apple, and Huawei, and smartphone sales represented 78 percent of total mobile phone sales. For feature phones (or "dumbphones") as of 2016, the largest were Samsung, Nokia, and Alcatel.
source:https://en.wikipedia.org/wiki/Mobile_phone
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, cross_val_score, KFold, GridSearchCV, StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, auc, recall_score, precision_score, f1_score
from sklearn.metrics import roc_auc_score, mean_squared_error, confusion_matrix, classification_report
from sklearn.feature_selection import SelectKBest, chi2
from catboost import CatBoostClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from yellowbrick.model_selection import CVScores
from pycaret.classification import *
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.offline import init_notebook_mode,iplot
import plotly as py
import cufflinks as cf
py.offline.init_notebook_mode(connected=True)
init_notebook_mode(connected=True)
init_notebook_mode(connected=True)
cf.go_offline()
import warnings
warnings.filterwarnings('ignore')
data=pd.read_csv("Cellphone Price Range.csv")
data.head(6)
| battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | price_range | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 842 | 0 | 2.2 | 0 | 1 | 0 | 7 | 0.6 | 188 | 2 | 2 | 20 | 756 | 2549 | 9 | 7 | 19 | 0 | 0 | 1 | 1 |
| 1 | 1021 | 1 | 0.5 | 1 | 0 | 1 | 53 | 0.7 | 136 | 3 | 6 | 905 | 1988 | 2631 | 17 | 3 | 7 | 1 | 1 | 0 | 2 |
| 2 | 563 | 1 | 0.5 | 1 | 2 | 1 | 41 | 0.9 | 145 | 5 | 6 | 1263 | 1716 | 2603 | 11 | 2 | 9 | 1 | 1 | 0 | 2 |
| 3 | 615 | 1 | 2.5 | 0 | 0 | 0 | 10 | 0.8 | 131 | 6 | 9 | 1216 | 1786 | 2769 | 16 | 8 | 11 | 1 | 0 | 0 | 2 |
| 4 | 1821 | 1 | 1.2 | 0 | 13 | 1 | 44 | 0.6 | 141 | 2 | 14 | 1208 | 1212 | 1411 | 8 | 2 | 15 | 1 | 1 | 0 | 1 |
| 5 | 1859 | 0 | 0.5 | 1 | 3 | 0 | 22 | 0.7 | 164 | 1 | 7 | 1004 | 1654 | 1067 | 17 | 1 | 10 | 1 | 0 | 0 | 1 |
datahead=data.head(6)
table_fig = ff.create_table(datahead, height_constant=35)
table_fig.layout.width=2500
for i in range(len(table_fig.layout.annotations)):
table_fig.layout.annotations[i].font.size = 15
iplot(table_fig)
data.profile_report()